A classic tension exists in the design of automated data processing systems between pure client-server based systems, such as computer mainframe systems or the World Wide Web, and pure distributed systems, such as Networks of Workstations (NOWS) that are used to solve complex computer problems, such as modeling atomic blasts or breaking cryptographic keys.
Client-server systems are popular because they rely on a clean division of responsibility between the server and the client. The server is often costly and specially managed, since it performs computations or stores data for a large number of clients. Each client is inexpensive, having only the local resources needed to interact with the user of the system. A network of reasonable performance is assumed to connect the server and the client. The economic model of these systems is that of centralized management and control driving down the incremental cost of deploying client systems.
However, this model has significant costs that must be considered. For instance, the incremental cost of adding a new client system may be quite high. Additional network capacity must be available, sufficient computing resources must be available to support that client, including storage, memory and computing cycles, and additional operational overhead is needed for each client because of these additional resources. As the central servers become larger and more complex they become much less reliable. Finally, a system failure of the server results in all clients losing service.
Distributed systems are popular because the resources of the system are distributed to each client, which enables more complex functionality within the client. Access to programs or data is faster since they are located with the client, reducing load on the network itself. The system is more reliable, since the failure of a node affects only it. Many computing tasks are easily broken down into portions that can be independently calculated, and these portions are cheaply distributed among the systems involved. This also reduces network bandwidth requirements and limits the impact of a failed node.
On the other hand, a distributed system is more complex to administer, and it may be more difficult to diagnose and solve hardware or software failures.
Television viewing may be modeled as a client-server system, but one where the server-to-client network path is for all intents and purposes of infinite speed, and where the client-to-server path is incoherent and unmanaged. This is a natural artifact of the broadcast nature of television. The cost of adding another viewer is zero, and the service delivered is the same as that delivered to all other viewers.
There have been, and continue to be, many efforts to deliver television programming over computer networks, such as the Internet, or even over a local cable television plant operating as a network. The point-to-point nature of computer networks makes these efforts unwieldy and expensive, since additional resources are required for each additional viewer. Fully interactive television systems, where the viewer totally controls video streaming bandwidth through a client settop device, have proven even more uneconomical because dedication of server resources to each client quickly limits the size of the system that can be profitably built and managed.
However, television viewers show a high degree of interest in choice and control over television viewing. This interest results in the need for the client system to effectively manage the memory demands of program material that a viewer wants to record. Additionally, the management of recording desired program material is of equal importance to the memory management task.
It would be advantageous to provide a data storage management and scheduling system that manages the available data space on a storage medium and any input sources. It would further be advantageous to provide a data storage management and scheduling system that efficiently schedules the insertion and deletion of data on a medium.